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CPPpred-En:整体框架整合了蛋白质语言模型和常规特征,用于高精度的细胞透预测

Yong Eun Jang¹, Minjun Kwon¹, Seok Gi Kim¹

¹Department of Molecular Science and Technology and Department of Physiology, Ajou University, Suwon, 16499, Republic of Korea; Department of Physiology, Ajou University School of Medicine, Suwon, 16499, Republic of Korea.

Computers in biology and medicine

|June 21, 2025

概括

我们开发了CPPpred-En,这是一个整体模型,通过结合各种特征,准确预测细胞透 (CPP). 这种工具增强了药物输送和向治疗的开发.

科学领域:

生物化学和分子生物学
计算生物学和生物信息学
药物发现和开发药物发现和开发

背景情况:

细胞透 (CPP) 对于药物输送至关重要,因为它们具有穿透细胞膜的能力.
准确的CPP预测对于推进基于的疗法至关重要.
现有的预测方法往往无法有效地整合各种特征.

研究的目的:

为细胞透 (CPPs) 开发一个先进的预测模型.
提高治疗应用中CPP识别的准确性和可靠性.

主要方法:

提出了CPPpred-En,这是一个集体学习模型,集成了传统的特征和基于蛋白质语言模型 (PLM) 的特征.
评估了多个机器学习分类器,并选择了最佳的功能-分类器组合.
在CPP924和MLCPP 2.0数据集上训练并验证了模型.

主要成果:

CPPpred-En实现了高精度 (97.27% Acc,0.964 MCC 在 CPP924;96.10% Acc,0.707 MCC 在 MLCPP 2.0).
整体策略在不同的数据集中展示了强大的概括性.
超过现有的最先进的CPP预测方法.

结论:

通过集体学习集成传统和PLM功能是改善CPP预测的强大策略.
CPPpred-En是一个非常准确和可靠的工具,用于识别CPP.
这一进步对药物输送和向疗法开发具有重大前景.

关键词:

生物信息学是一种生物信息学.细胞透可以穿透细胞.组合学习学习组合学习功能编码功能编码.机器学习机器学习类预测预测类预测蛋白质语言模型的模型

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